DGM - AI/ML Engineer - AI for Automotive Design

4 days ago

Pune, Maharashtra, India Michael Page Full time
Opportunity to work with a leading Manufacturing OrganizationGreat working culture

About Our Client

Leading global automobile manufacturing company. Its diverse portfolio includes an extensive range of cars, sports utility vehicles, trucks, buses and defence vehicles.

Job Description

Develop and implement AI/ML models for generative automotive design: Leverage Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, and other generative AI techniques to create novel and innovative vehicle design concepts (exterior, interior, components) based on diverse inputs (text prompts, sketches, performance parameters).Pioneer Sketch-to-3D Model & Surface-to-3D Conversion: Research, develop, and deploy AI models that can accurately transform 2D sketches, images, and conceptual surfaces into high-fidelity 3D automotive models, streamlining the early design phase.Advance AI-driven Content Generation for Design: Explore and implement AI models for generating design-related content, including visual assets, material renderings, and virtual environment elements, from textual descriptions or design constraints.Integrate AI with CAD/CAE Workflows: Work closely with design and engineering teams to seamlessly integrate AI-generated outputs and insights into existing CAD (, CATIA, SolidWorks) and CAE (, ANSYS, Abaqus) software environments.Optimize Design through AI-driven Simulation: Apply AI/ML for multi-objective design optimization, including aerodynamic performance, structural integrity (FEA), lightweighting, thermal management, and NVH characteristics, using techniques like Bayesian Optimization and Genetic Algorithms.Automate Feature Engineering and Data Curation: Develop robust data pipelines and feature engineering strategies for large, complex automotive design datasets (3D geometries, material properties, performance data, simulation results).Research & Innovation: Stay abreast of the latest advancements in AI, machine learning, computer vision, and computational design, continuously evaluating and recommending new technologies and methodologies.Collaboration: Work cross-functionally with industrial designers, aerodynamicists, structural engineers, materials scientists, and software developers to translate design visions into tangible, AI-powered solutions.Model Deployment & Monitoring: Contribute to the deployment of AI models into production environments and establish monitoring systems for performance, quality, and continuous improvement

The Successful Applicant

AI/ML Expertise (5+ years professional experience):
○ Strong theoretical and practical knowledge of Deep Learning architectures (CNNs, RNNs, Transformers).
○ Proven experience with Generative AI models (GANs, VAEs, Diffusion Models) and their application in creative or design contexts.
○ Proficiency in Computer Vision techniques, including object detection, segmentation, and 3D reconstruction.
○ Experience with optimization algorithms (, Genetic Algorithms, Bayesian Optimization).
○ Solid understanding of MLOps principles for model development, deployment, and lifecycle management.
● Programming Proficiency:
○ Expert-level proficiency in Python, including deep learning frameworks (TensorFlow, PyTorch) and data science libraries (NumPy, Pandas, scikit-learn).
○ Familiarity with C++ is a plus.
● Automotive Design & Engineering Domain Knowledge:
○ Fundamental understanding of automotive design principles, aesthetics, ergonomics, and styling.
○ Familiarity with CAD software (, CATIA, SolidWorks, Autodesk Fusion 360) and CAE simulation concepts (CFD, FEA).
○ Basic understanding of automotive manufacturing processes and material properties.
● Problem-Solving & Creativity: Exceptional analytical and problem-solving skills with a demonstrated ability to tackle complex, ambiguous design challenges using AI.
● Communication: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to diverse audiences, including non-technical design and engineering teams.
● Collaboration: Proven ability to work effectively in a collaborative, fast-paced, and interdisciplinary team environment.

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